Overview

Dataset statistics

Number of variables16
Number of observations19845
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory2.5 MiB
Average record size in memory132.0 B

Variable types

NUM7
CAT7
DATE2

Warnings

Dataset has 1 (< 0.1%) duplicate rows Duplicates
Id.Escala has a high cardinality: 17547 distinct values High cardinality
Buque has a high cardinality: 1703 distinct values High cardinality
buque_def is highly correlated with Tipo BuqueHigh correlation
Tipo Buque is highly correlated with buque_defHigh correlation
Muelle Atraque Real is highly correlated with terminalHigh correlation
terminal is highly correlated with Muelle Atraque RealHigh correlation
Id.Escala is uniformly distributed Uniform

Reproduction

Analysis started2020-10-22 08:47:57.199277
Analysis finished2020-10-22 08:48:16.346787
Duration19.15 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Año Servicio
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size155.0 KiB
2017
6818 
2018
6626 
2019
6401 
ValueCountFrequency (%) 
2017681834.4%
 
2018662633.4%
 
2019640132.3%
 
2020-10-22T10:48:16.498702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-22T10:48:16.583003image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:16.684926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length4
Min length4

Id.Escala
Categorical

HIGH CARDINALITY
UNIFORM

Distinct17547
Distinct (%)88.4%
Missing0
Missing (%)0.0%
Memory size155.0 KiB
1 2018 2676
 
4
1 2019 130
 
3
1 2019 134
 
3
1 2017 6741
 
3
1 2018 5574
 
3
Other values (17542)
19829 
ValueCountFrequency (%) 
1 2018 26764< 0.1%
 
1 2019 1303< 0.1%
 
1 2019 1343< 0.1%
 
1 2017 67413< 0.1%
 
1 2018 55743< 0.1%
 
1 2018 19023< 0.1%
 
1 2017 24243< 0.1%
 
1 2018 9353< 0.1%
 
1 2017 39043< 0.1%
 
1 2018 16663< 0.1%
 
Other values (17537)1981499.8%
 
2020-10-22T10:48:16.885833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique15278 ?
Unique (%)77.0%
2020-10-22T10:48:17.045744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length21
Median length21
Mean length21
Min length21

Clave Lloyd o Num. OMI
Real number (ℝ≥0)

Distinct1644
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9290157.713
Minimum5142657
Maximum9842097
Zeros0
Zeros (%)0.0%
Memory size77.5 KiB
2020-10-22T10:48:18.336438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum5142657
5-th percentile9004231
Q19214202
median9323637
Q39458523
95-th percentile9673630
Maximum9842097
Range4699440
Interquartile range (IQR)244321

Descriptive statistics

Standard deviation324143.2868
Coefficient of variation (CV)0.03489104241
Kurtosis17.36700963
Mean9290157.713
Median Absolute Deviation (MAD)128082
Skewness-3.381161977
Sum-320413906
Variance1.050688704e+11
MonotocityNot monotonic
2020-10-22T10:48:18.513319image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
94987438284.2%
 
94585238074.1%
 
92633707563.8%
 
78272254052.0%
 
91341394002.0%
 
92043623781.9%
 
92434233631.8%
 
94411302671.3%
 
91437902511.3%
 
94655382141.1%
 
Other values (1634)1517676.5%
 
ValueCountFrequency (%) 
51426573< 0.1%
 
53833041< 0.1%
 
66028981< 0.1%
 
72259103< 0.1%
 
73562521< 0.1%
 
ValueCountFrequency (%) 
98420971< 0.1%
 
98420851< 0.1%
 
98420612< 0.1%
 
98305991< 0.1%
 
98297221< 0.1%
 

Buque
Categorical

HIGH CARDINALITY

Distinct1703
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Memory size155.0 KiB
FORZA
 
807
CIUDAD DE IBIZA
 
756
VISEMAR ONE
 
532
REGINA BALTICA
 
405
SVEN
 
400
Other values (1698)
16945 
ValueCountFrequency (%) 
FORZA 8074.1%
 
CIUDAD DE IBIZA 7563.8%
 
VISEMAR ONE 5322.7%
 
REGINA BALTICA 4052.0%
 
SVEN 4002.0%
 
SUPER FAST LEVANTE 3781.9%
 
NAPOLES 3631.8%
 
HEDY LAMARR 2961.5%
 
ABEL MATUTES 2671.3%
 
YAKOOT 2511.3%
 
Other values (1693)1539077.6%
 
2020-10-22T10:48:18.719204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique622 ?
Unique (%)3.1%
2020-10-22T10:48:18.880114image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length20
Median length20
Mean length20
Min length20

Tipo Buque
Categorical

HIGH CORRELATION

Distinct25
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size155.0 KiB
PORTACONTENEDOR
10908 
FERRYS RO/RO PASAJEROS
4340 
RO/RO VEHÍCULO
1449 
POLIV. GENERAL CONTENEDOR
 
727
RO/RO CARGA GENERAL
 
665
Other values (20)
1756 
ValueCountFrequency (%) 
PORTACONTENEDOR 1090855.0%
 
FERRYS RO/RO PASAJEROS 434021.9%
 
RO/RO VEHÍCULO 14497.3%
 
POLIV. GENERAL CONTENEDOR7273.7%
 
RO/RO CARGA GENERAL 6653.4%
 
CRUCERO TURÍSTICO 5983.0%
 
RO/RO LO/LO 4132.1%
 
OTROS GRANELEROS 1670.8%
 
PRODUCTOS QUÍMICOS 1600.8%
 
MERCANT. CONVENCIONAL 1300.7%
 
Other values (15)2881.5%
 
2020-10-22T10:48:19.017057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-10-22T10:48:19.171952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length25
Median length25
Mean length25
Min length25

buque_def
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size155.0 KiB
contenedor
11780 
ropax
4375 
roro
2610 
crucero
 
598
otros
 
482
ValueCountFrequency (%) 
contenedor1178059.4%
 
ropax437522.0%
 
roro261013.2%
 
crucero5983.0%
 
otros4822.4%
 
2020-10-22T10:48:19.312872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-22T10:48:19.408819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:19.539766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length7.896749811
Min length4

terminal
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size155.0 KiB
csp
6179 
apm
3670 
balea_otros
3030 
msc
2483 
vte
2340 
Other values (2)
2143 
ValueCountFrequency (%) 
csp617931.1%
 
apm367018.5%
 
balea_otros303015.3%
 
msc248312.5%
 
vte234011.8%
 
trasme_cru19359.8%
 
ampli_norte2081.0%
 
2020-10-22T10:48:19.666675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-22T10:48:19.753626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:19.908559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length3
Mean length4.987855883
Min length3

Muelle Atraque Real
Categorical

HIGH CORRELATION

Distinct25
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size155.0 KiB
PRINCIPE FELIPE
5257 
TRANSVERSAL COSTA
2483 
DIQUE DEL ESTE
1488 
ESP.TUR.SUR-1
1340 
LEVANTE 1
1235 
Other values (20)
8042 
ValueCountFrequency (%) 
PRINCIPE FELIPE 525726.5%
 
TRANSVERSAL COSTA 248312.5%
 
DIQUE DEL ESTE 14887.5%
 
ESP.TUR.SUR-1 13406.8%
 
LEVANTE 1 12356.2%
 
TRANSVERSALES 10545.3%
 
TURIA 10195.1%
 
LEVANTE 4 8554.3%
 
PONIENTE-1 7833.9%
 
LLOVERA 6813.4%
 
Other values (15)365018.4%
 
2020-10-22T10:48:20.053458image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-10-22T10:48:20.181387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length25
Median length25
Mean length25
Min length25

Num. Atraque
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.226404636
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Memory size155.0 KiB
2020-10-22T10:48:20.287328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum8
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5408044566
Coefficient of variation (CV)0.4409673943
Kurtosis11.44298686
Mean1.226404636
Median Absolute Deviation (MAD)0
Skewness2.950783778
Sum24338
Variance0.2924694603
MonotocityNot monotonic
2020-10-22T10:48:20.394288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
11626181.9%
 
2287514.5%
 
35522.8%
 
41230.6%
 
5270.1%
 
66< 0.1%
 
81< 0.1%
 
ValueCountFrequency (%) 
11626181.9%
 
2287514.5%
 
35522.8%
 
41230.6%
 
5270.1%
 
ValueCountFrequency (%) 
81< 0.1%
 
66< 0.1%
 
5270.1%
 
41230.6%
 
35522.8%
 
Distinct19126
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Memory size155.0 KiB
Minimum2017-01-01 14:30:00
Maximum2019-12-13 00:30:00
2020-10-22T10:48:21.642720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:21.986529image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct19324
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Memory size155.0 KiB
Minimum2017-01-02 14:00:00
Maximum2019-12-13 10:05:00
2020-10-22T10:48:22.155453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:22.338351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Amarre calculado (horas)
Real number (ℝ≥0)

Distinct1532
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.56083312
Minimum0.1666666667
Maximum438.3333333
Zeros0
Zeros (%)0.0%
Memory size155.0 KiB
2020-10-22T10:48:22.506237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.1666666667
5-th percentile2.5
Q18.083333333
median14.5
Q322.41666667
95-th percentile39.58333333
Maximum438.3333333
Range438.1666667
Interquartile range (IQR)14.33333333

Descriptive statistics

Standard deviation18.57789478
Coefficient of variation (CV)1.057916481
Kurtosis78.68603863
Mean17.56083312
Median Absolute Deviation (MAD)7
Skewness6.683930839
Sum348494.7333
Variance345.1381744
MonotocityNot monotonic
2020-10-22T10:48:22.653155image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3.52681.4%
 
3.5833333332451.2%
 
3.3333333332061.0%
 
3.4166666671891.0%
 
3.751320.7%
 
3.4166666671270.6%
 
3.1666666671160.6%
 
3.6666666671110.6%
 
15850.4%
 
2.083333333800.4%
 
Other values (1522)1828692.1%
 
ValueCountFrequency (%) 
0.16666666671< 0.1%
 
0.25000000011< 0.1%
 
0.41666666661< 0.1%
 
0.41666666681< 0.1%
 
0.49999999991< 0.1%
 
ValueCountFrequency (%) 
438.33333331< 0.1%
 
366.16666671< 0.1%
 
334.33333331< 0.1%
 
322.251< 0.1%
 
314.08333331< 0.1%
 

Potencia
Real number (ℝ≥0)

Distinct652
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26387.19048
Minimum1
Maximum88200
Zeros0
Zeros (%)0.0%
Memory size155.0 KiB
2020-10-22T10:48:22.815084image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5300
Q111060
median21600
Q336560
95-th percentile68520
Maximum88200
Range88199
Interquartile range (IQR)25500

Descriptive statistics

Standard deviation18798.00976
Coefficient of variation (CV)0.7123914832
Kurtosis0.007637946613
Mean26387.19048
Median Absolute Deviation (MAD)11611
Skewness0.9958624521
Sum523653795
Variance353365170.9
MonotocityNot monotonic
2020-10-22T10:48:22.960003image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2520016208.2%
 
973011665.9%
 
216008484.3%
 
200708374.2%
 
53005893.0%
 
189004552.3%
 
474304172.1%
 
191244052.0%
 
252043781.9%
 
722403601.8%
 
Other values (642)1277064.3%
 
ValueCountFrequency (%) 
13< 0.1%
 
6001< 0.1%
 
8162< 0.1%
 
8501< 0.1%
 
9141< 0.1%
 
ValueCountFrequency (%) 
882001< 0.1%
 
809051< 0.1%
 
800803< 0.1%
 
756243< 0.1%
 
75600230.1%
 

Det. G.T. Buque
Real number (ℝ≥0)

Distinct974
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38622.5618
Minimum682
Maximum228081
Zeros0
Zeros (%)0.0%
Memory size155.0 KiB
2020-10-22T10:48:23.105921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum682
5-th percentile6362
Q116686
median26375
Q351714
95-th percentile107849
Maximum228081
Range227399
Interquartile range (IQR)35028

Descriptive statistics

Standard deviation34654.76469
Coefficient of variation (CV)0.897267376
Kurtosis2.599216663
Mean38622.5618
Median Absolute Deviation (MAD)15829
Skewness1.65303246
Sum766464739
Variance1200952715
MonotocityNot monotonic
2020-10-22T10:48:23.263833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
263758284.2%
 
255188074.1%
 
166867563.8%
 
99577153.6%
 
63625893.0%
 
244094552.3%
 
183454052.0%
 
326474042.0%
 
175053781.9%
 
326453321.7%
 
Other values (964)1417671.4%
 
ValueCountFrequency (%) 
6822< 0.1%
 
6938< 0.1%
 
7231< 0.1%
 
9981< 0.1%
 
11891< 0.1%
 
ValueCountFrequency (%) 
2280811< 0.1%
 
1948494< 0.1%
 
1943087< 0.1%
 
1942507< 0.1%
 
1934894< 0.1%
 

Potencia_kW
Real number (ℝ≥0)

Distinct663
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1085.052452
Minimum0.13
Maximum15317.03
Zeros0
Zeros (%)0.0%
Memory size155.0 KiB
2020-10-22T10:48:23.414749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.13
5-th percentile313.3967749
Q1534.3892842
median750
Q31187.463743
95-th percentile1924.102411
Maximum15317.03
Range15316.9
Interquartile range (IQR)653.0744585

Descriptive statistics

Standard deviation1408.84444
Coefficient of variation (CV)1.298411369
Kurtosis40.39590071
Mean1085.052452
Median Absolute Deviation (MAD)436.6032251
Skewness5.837692867
Sum21532865.91
Variance1984842.655
MonotocityNot monotonic
2020-10-22T10:48:23.576658image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
313.3967749405820.4%
 
800224611.3%
 
75015187.6%
 
1650.29477313656.9%
 
768.077375113326.7%
 
1844.25654112636.4%
 
534.389284211876.0%
 
1187.46374310865.5%
 
70010795.4%
 
1679.53875810415.2%
 
Other values (653)367018.5%
 
ValueCountFrequency (%) 
0.133< 0.1%
 
132.62< 0.1%
 
145.531< 0.1%
 
1561< 0.1%
 
163.81< 0.1%
 
ValueCountFrequency (%) 
15317.03150.1%
 
137601< 0.1%
 
13622.45< 0.1%
 
13409.55150.1%
 
13318.21< 0.1%
 

Manoeuvring
Real number (ℝ≥0)

Distinct710
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7955.391578
Minimum0.35
Maximum26294.125
Zeros0
Zeros (%)0.0%
Memory size155.0 KiB
2020-10-22T10:48:23.740567image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.35
5-th percentile1722.5
Q13534.08
median5644.8
Q311882
95-th percentile22268.675
Maximum26294.125
Range26293.775
Interquartile range (IQR)8347.92

Descriptive statistics

Standard deviation6132.608143
Coefficient of variation (CV)0.7708744546
Kurtosis0.08888164541
Mean7955.391578
Median Absolute Deviation (MAD)2482.55
Skewness1.120130843
Sum157874745.9
Variance37608882.64
MonotocityNot monotonic
2020-10-22T10:48:23.902457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
5644.816208.2%
 
3162.2511665.9%
 
4838.48484.3%
 
6422.48374.2%
 
1722.55893.0%
 
4233.64552.3%
 
15414.754172.1%
 
4283.7764052.0%
 
8065.283781.9%
 
234783601.8%
 
Other values (700)1277064.3%
 
ValueCountFrequency (%) 
0.353< 0.1%
 
1951< 0.1%
 
227.1361< 0.1%
 
237.448< 0.1%
 
265.22< 0.1%
 
ValueCountFrequency (%) 
26294.1251< 0.1%
 
260263< 0.1%
 
24297200.1%
 
234783601.8%
 
23477.675120.1%
 

Interactions

2020-10-22T10:48:03.812404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:06.170965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:06.308890image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:06.443832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:06.577740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:06.715680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:06.866576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:07.104462image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:07.252380image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:07.395300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:07.531205image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:07.665149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:07.814047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:07.967981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:08.111900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:08.249822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:08.380749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:08.509677image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:08.645583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:08.776508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:08.928425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:09.072363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:09.216262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:09.379174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:09.521095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:09.660015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:09.820925image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:09.966843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:10.102787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:10.242689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:10.379613image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:10.507540image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:10.631492image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:10.758420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:10.899342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:11.036265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:11.189728image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:11.349638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:11.496557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:11.638458image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:11.804388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:11.973289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:12.255132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:12.396034image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:12.534975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:12.665904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:12.806825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:12.939750image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:13.086648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-10-22T10:48:24.044358image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-10-22T10:48:24.246245image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-10-22T10:48:24.441690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-10-22T10:48:24.646622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-10-22T10:48:24.860502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-10-22T10:48:13.421198image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T10:48:14.956065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

Año ServicioId.EscalaClave Lloyd o Num. OMIBuqueTipo Buquebuque_defterminalMuelle Atraque RealNum. AtraqueFH. Amarre CalcFH. Desamarre CalcAmarre calculado (horas)PotenciaDet. G.T. BuquePotencia_kWManoeuvring
020171 2016 64879356309CMA CGM HYDRAPORTACONTENEDORcontenedorapmLLOVERA12017-01-01 22:30:002017-01-03 13:30:0039.00000072240.0128600.01924.123478
120171 2016 66329509138FLEUR NPORTACONTENEDORcontenedorapmLEVANTE 112017-01-02 00:35:002017-01-03 20:00:0043.41666725040.035887.0768.0778138
220171 2016 66359449118MARTHA SCHULTEPORTACONTENEDORcontenedorapmLEVANTE 312017-01-01 17:50:002017-01-02 23:10:0029.33333331990.038364.01187.4610396.8
320171 2016 66749230490MSC LORETTAPORTACONTENEDORcontenedorcspPRINCIPE FELIPE12017-01-02 06:10:002017-01-03 10:35:0028.41666757075.073819.01844.2618549.4
420171 2016 66819007817MSC NAMIBIA IIPORTACONTENEDORcontenedorcspPRINCIPE FELIPE12017-01-04 14:30:002017-01-05 16:20:0025.83333313120.023953.0534.3894264
520171 2016 66929354466HELMUTPOLIV. GENERAL CONTENEDORcontenedorcspPRINCIPE FELIPE22017-01-01 16:30:002017-01-03 01:25:0032.9166678399.09981.0313.3972729.68
620171 2016 66939326964PASSATPORTACONTENEDORcontenedorapmLEVANTE 322017-01-11 11:50:002017-01-12 00:35:0012.7500008399.09990.0313.3972729.68
720171 2016 66939326964PASSATPORTACONTENEDORcontenedorcspPRINCIPE FELIPE32017-01-10 20:25:002017-01-11 10:55:0014.5000008399.09990.0313.3972729.68
820171 2016 66999290567MSC BRUXELLESPORTACONTENEDORcontenedorcspPRINCIPE FELIPE12017-01-01 21:00:002017-01-03 14:10:0041.16666768520.0107849.01679.5422269
920171 2016 67049321471YANKI APORTACONTENEDORcontenedorapmLEVANTE 312017-01-04 06:00:002017-01-05 01:25:0019.41666721770.027915.0768.0777075.25

Last rows

Año ServicioId.EscalaClave Lloyd o Num. OMIBuqueTipo Buquebuque_defterminalMuelle Atraque RealNum. AtraqueFH. Amarre CalcFH. Desamarre CalcAmarre calculado (horas)PotenciaDet. G.T. BuquePotencia_kWManoeuvring
1983520191 2019 63999673666VIOLETA BPORTACONTENEDORcontenedorcspM. ESTE (PRINCIPE FELIPE)12019-12-11 02:25:002019-12-11 17:05:0014.66666712200.018826.0534.3893965
1983620191 2019 64129498743HEDY LAMARRFERRYS RO/RO PASAJEROSropaxbalea_otrosTURIA12019-12-07 19:10:002019-12-08 22:05:0026.91666721600.026375.0629.7214838.4
1983720191 2019 64139498743HEDY LAMARRFERRYS RO/RO PASAJEROSropaxbalea_otrosTURIA12019-12-09 19:05:002019-12-09 22:10:003.08333321600.026375.08004838.4
1983820191 2019 64149435844LEONBUQUE TANQUE/MGotrosbalea_otrosESP.TUR.SUR-212019-12-06 00:05:002019-12-10 01:10:0097.0833333399.02997.01291.621189.65
1983920191 2019 64169498743HEDY LAMARRFERRYS RO/RO PASAJEROSropaxbalea_otrosTURIA12019-12-10 19:05:002019-12-10 22:10:003.08333321600.026375.08004838.4
1984020191 2019 64249237644CIUDAD DE CEUTAFERRYS RO/RO PASAJEROSropaxtrasme_cruTRANSVERSALES12019-12-05 23:35:002019-12-06 00:10:000.58333328320.06554.07506343.68
1984120191 2019 64309232656CHARLIEPORTACONTENEDORcontenedorcspM. ESTE (PRINCIPE FELIPE)12019-12-07 20:00:002019-12-08 20:05:0024.08333313328.016661.0534.3894331.6
1984220191 2019 64329326990ANALENAPORTACONTENEDORcontenedorcspPRINCIPE FELIPE12019-12-09 09:10:002019-12-11 03:05:0041.9166678399.09990.0313.3972729.68
1984320191 2019 64629137997ROSALIND FRANKLINFERRYS RO/RO PASAJEROSropaxbalea_otrosTURIA12019-12-11 19:40:002019-12-11 22:25:002.75000023040.033958.08005160.96
1984420191 2019 64849498743HEDY LAMARRFERRYS RO/RO PASAJEROSropaxbalea_otrosTURIA12019-12-12 19:10:002019-12-12 22:25:003.25000021600.026375.08004838.4

Duplicate rows

Most frequent

Año ServicioId.EscalaClave Lloyd o Num. OMIBuqueTipo Buquebuque_defterminalMuelle Atraque RealNum. AtraqueFH. Amarre CalcFH. Desamarre CalcAmarre calculado (horas)PotenciaDet. G.T. BuquePotencia_kWManoeuvringcount
020171 2017 33439498743VISEMAR ONEFERRYS RO/RO PASAJEROSropaxbalea_otrosTURIA12017-06-23 18:45:002017-06-23 22:30:003.7521600.026375.0800.04838.42